Performance Analysis of Distributed Decision Fusion Using a Censoring Scheme in Wireless Sensor Networks

Abstract

Sensor-censoring schemes have been widely applied to distributed detection to achieve the goal of energy saving in limited-energy wireless sensor networks (WSNs). In the traditional censoring scheme, the sensor transmits data to the fusion center (FC) only when the reliability is beyond a specified threshold, and hereby, energy saving is achieved. To further exploit the energy-efficiency capability of the censoring decision scheme, this paper proposes a new multilevel sensor-censoring scheme. As opposed to our earlier proposed three-region censoring scheme, the number of censoring levels in the proposed scheme is not restricted. A criterion on computing the reliability of the observation in each region is quantitatively determined, which controls the power of the signal transmitted to the FC. When the observation falls within the region with high reliability, the power of the transmitted signal is high, and vice versa. Both soft- and hard-decision fusion rules under the considered multilevel censoring strategy are investigated. For a given fusion rule, the main problem of this work is to minimize the error probability of the global decisions made by the FC by obtaining the best region allocation on the observation, which corresponds to the optimum multilevel censoring regions. The performance of the proposed multilevel censoring scheme is examined in terms of both energy saving and error performance. We compare the multilevel censoring scheme with the conventional scheme, which censors no sensor observations. The results show that the multilevel censoring scheme not only offers us a more flexible design of the censoring strategy but consumes much less energy as well, compared with the conventional scheme when the same error probability constraint is given. In addition, the obtained result shows that the superiority of the multilevel censoring scheme becomes more remarkable when the signal strength of the sensor observations is small.